To better understand new and rare astronomical phenomena, radio astronomers are adopting accelerated computing and AI on NVIDIA Holoscan and IGX platforms.
Transformers have gained significant attention due to their powerful capabilities in understanding and generating human-like text, making them suitable for various applications like language translation, summarization, and creative content generation. They operate based on an attention mechanism, which determines how much focus each token in a sequence should have on others to make informed predictions. While they offer great promise, the challenge lies in optimizing these models to handle large amounts of data efficiently without excessive computational costs.
A significant challenge in developing transformer models is their inefficiency when handling long text sequences. As the context length increases, the computational and memory requirements grow exponentially. This happens because each token interacts with every other token in the sequence, leading to quadratic complexity that quickly becomes unmanageable. This limitation constrains the application of transformers in tasks that demand long contexts, such as language modeling and document summarization, where retaining and processing the entire sequence is crucial for maintaining context and coherence. Thus, solutions are needed to reduce the computational burden while retaining the model’s effectiveness.
Approaches to address this issue have included sparse attention mechanisms, which limit the number of interactions between tokens, and context compression techniques that reduce the sequence length by summarizing past information. These methods attempt to reduce the number of tokens considered in the attention mechanism but often do so at the cost of performance, as reducing context can lead to a loss of critical information. This trade-off between efficiency and performance has prompted researchers to explore new methods to maintain high accuracy while reducing computational and memory requirements.
Yoshua Bengio played a crucial role in the development of the machine-learning systems we see today. Now, he says that they could pose an existential risk to humanity.
Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.
ChatGPT-4 passes the Turing Test, marking a new milestone in AI. Explore the implications of AI-human interaction.
Quantum AI, the fusion of quantum computing and artificial intelligence, is poised to revolutionize industries from finance to healthcare.
Neuromorphic engineering is a cutting-edge field that focuses on developing computer hardware and software systems inspired by the structure, function, and behavior of the human brain. The ultimate goal is to create computing systems that are significantly more energy-efficient, scalable, and adaptive than conventional computer systems, capable of solving complex problems in a manner reminiscent of the brain’s approach.
This interdisciplinary field draws upon expertise from various domains, including neuroscience, computer science, electronics, nanotechnology, and materials science. Neuromorphic engineers strive to develop computer chips and systems incorporating artificial neurons and synapses, designed to process information in a parallel and distributed manner, akin to the brain’s functionality.
Key challenges in neuromorphic engineering encompass developing algorithms and hardware capable of performing intricate computations with minimal energy consumption, creating systems that can learn and adapt over time, and devising methods to control the behavior of artificial neurons and synapses in real-time.
The Nobel Prize in Physics has been awarded to two scientists, Geoffrey Hinton and John Hopfield, for their work on machine learning.
British-Canadian Professor Hinton is sometimes referred to as the “Godfather of AI” and said he was flabbergasted.
He resigned from Google in 2023, and has warned about the dangers of machines that could outsmart humans.
Two of San Francisco’s leading players in artificial intelligence have challenged the public to come up with questions capable of testing the capabilities of large language models (LLMs) like Google Gemini and OpenAI’s o1. Scale AI, which specializes in preparing the vast tracts of data on which the LLMs are trained, teamed up with the Center for AI Safety (CAIS) to launch the initiative, Humanity’s Last Exam.
Featuring prizes of US$5,000 (£3,800) for those who come up with the top 50 questions selected for the test, Scale and CAIS say the goal is to test how close we are to achieving “expert-level AI systems” using the “largest, broadest coalition of experts in history.”
Why do this? The leading LLMs are already acing many established tests in intelligence, mathematics and law, but it’s hard to be sure how meaningful this is. In many cases, they may have pre-learned the answers due to the gargantuan quantities of data on which they are trained, including a significant percentage of everything on the internet.
